ROC-AUC

Summary

A curve and single-number summary (Area Under Curve) of a binary classifier's performance across all classification thresholds — useful for comparing models independent of a chosen threshold.

How to Learn This

1Plot an ROC curve for a trained classifier and interpret the AUC value.
2Learn the difference between ROC-AUC and precision-recall AUC, and when each is more informative.
3Understand what AUC = 0.5 vs. AUC = 1.0 actually mean.

Evaluation Metrics (Accuracy, Precision, Recall, F1)

Confusion Matrix

More topics in Feature Engineering & Model Evaluation

Feature Scaling & Normalization One-Hot & Label Encoding Feature Selection Handling Imbalanced Data Hyperparameter Tuning (Grid/Random Search)Ensemble Methods (Bagging, Boosting)XGBoost & LightGBM Evaluation Metrics (Accuracy, Precision, Recall, F1)Confusion Matrix Bias-Variance Tradeoff Overfitting & Regularization (L1/L2)

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